Model documentation: SCOPE

In general, Fraunhofer IEE’s energy system models must be differentiated in their application purpose between the determination of investment decisions and detailed deployment planning.

The SCOPE Path and SCOPE SD model variants enable the determination of installed capacities in future cost-optimized energy sector-spanning scenarios for Europe. They differ in the closed path optimization with lower temporal and spatial resolution and the detailed determination of individual support years.

The SCOPE EM electricity market model enables detailed analyses of pricing on the European electricity markets and grid node-specific analyses in the German transmission grid on the basis of these determined capacities.

The geographical scope of Europe is the EU27 including Great Britain, Norway, Switzerland and excluding Malta and Cyprus.

Figure 1: Spatial resolution of the SCOPE SD model

Within Germany, possible divisions into market areas (zones) can be taken into account. The regional resolution of the market results in Germany then refers downstream to individual grid nodes.

Figure 2: Schematic representation of the transmission grid with selection of critical grid elements; using the year 2030 as an example (source Fraunhofer IEE); in the 3-zone variant based on https://www.acer.europa.eu/Individual%20Decisions_annex/ACER%20Decision%2011-2022%20on%20alternative%20BZ%20configurations%20-%20Annex%20I.pdf

Energy supply

The SCOPE Path and SCOPE SD energy system model makes expansion decisions in the areas of electricity supply, district heating supply and industrial process heat supply (hot water and steam). Optionally, the expansion decisions in the building heating supply and road transport can also be optimized. This is an annual plan with perfect foresight. A linear optimization problem is set up, which is solved by the commercial solver Gurobi.

The SCOPE-EM electricity market model optimizes the use of systems to meet electricity demand, reserve balancing power and cover demand for heat and transport. In order to carry out modeling based on the processes on the electricity market, the hours of the year are run through using rolling planning. This means that a period of 48 hours, for example, is optimized, which is then shifted forward 24 hours for the next optimization step. Storage levels as well as shutdown and start-up times of power plants are transferred to the next optimization step. A mixed integer linear optimization problem is set up, which is solved by the commercial solver Gurobi. The SCOPE-EM model itself is implemented in Matlab.

The basis for this is high-resolution spatial data and time series. In the area of renewable electricity generation potential and demand, this is covered at Fraunhofer IEE by the energyANTS simulation model.

Depending on scenarios, economic boundary conditions can be specified indirectly via exogenous demand developments.

Energy Demand

The input data for the energy system and electricity market models are shown in the following diagram.

Industry

Demand trends and potential targets are taken exogenously from bottom-up simulations and published studies. Industrial process heat in the area of hot water and steam is optimally covered endogenously. Furnaces and material use are specified.

Buildings

Optionally, demand trends and potential targets can be taken from bottom-up simulations and published studies or be determined for Germany using the AgentHomeID building stock model.

Transport

Alternatively, demand trends and potential targets can be taken from bottom-up simulations and published studies or determined for Germany using the AgentCarD vehicle stock model.

Flexibility (storage, DSM, grids)

Flexibilities in the electricity, heating and transport sectors are differentiated between seasonal and short-term and mapped using validated aggregation models.

Policy instruments and measures

Political boundary conditions can be evaluated by means of potential limits, electricity cost components, financing costs, etc. in terms of their impact on investment decisions and plant deployment in the electricity market.